Combustion optimization control method and system of garbage incinerator based on machine vision

Abstract

The invention discloses a combustion optimization control method and system for a garbage incinerator based on machine vision, and relates to the technical field of garbage incineration. The method comprises the steps that multispectral flame images of all air chambers in the garbage incinerator are captured; generating a flame panoramic image of each air chamber according to the multispectral flame image of each air chamber; based on the recognition result of the flame area, the combustion state of flames in all the air chambers and the air channeling state between all the air chambers are determined; based on the recognition result of the garbage area, the staying state of garbage in each air chamber is determined; according to the combustion state of flames in the air chambers, the air channeling state between the air chambers and the staying state of garbage in the air chambers, air supply parameters of the air chambers are adjusted; and multi-spectral flame images of all the air chambers in the adjusted garbage incinerator are monitored in real time, the future states of all the air chambers in the garbage incinerator are predicted through a deep learning network, and therefore air distribution and combustion cooperative control parameters between all the air chambers are optimized.

Description

Technical Field

[0001] The present application relates to the technical field of waste incineration, and in particular to a combustion optimization control method and system for a waste incinerator based on machine vision. Background Art

[0002] As an important facility for treating urban solid waste, the efficient and stable operation of waste incinerators is of great significance for environmental protection and energy recovery. However, traditional waste incinerators face many challenges during the combustion process, such as low combustion efficiency, difficulty in controlling pollutant emissions, and complex operation.

[0003] Traditional waste incinerator control methods mainly rely on manual operation and experience judgment, which is subject to great subjectivity and error. Operators need to monitor the flame image of the incinerator at all times and make adjustments based on factors such as flame color and combustion status. This method is not only labor-intensive, but also difficult to achieve real-time and accurate control.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a combustion optimization control method and system for a waste incinerator based on machine vision to solve the above-mentioned technical problems.

[0006] The present application provides a method for optimizing combustion control of a waste incinerator based on machine vision, comprising: capturing a multispectral flame image of each wind chamber in the waste incinerator; generating a flame panoramic image of each wind chamber according to the multispectral flame image of each wind chamber; using an adaptive threshold segmentation algorithm to perform flame area recognition and garbage area recognition on the flame panoramic image of each wind chamber; determining the combustion state of the flame in each wind chamber and the air flow state between the wind chambers based on the recognition result of the flame area; determining the residence state of the garbage in each wind chamber based on the recognition result of the garbage area; adjusting the air supply parameters of each wind chamber according to the combustion state of the flame in each wind chamber, the air flow state between the wind chambers and the residence state of the garbage in each wind chamber; real-time monitoring of the adjusted multispectral flame images of each wind chamber in the waste incinerator, and predicting the future state of each wind chamber in the waste incinerator through a deep learning network, so as to optimize the air volume distribution and combustion coordinated control parameters between the wind chambers.

[0007] The present application provides a combustion optimization control system for a waste incinerator based on machine vision, comprising: a flame image capture module, used to capture multispectral flame images of each wind chamber in the waste incinerator; a flame panoramic image generation module, used to generate a flame panoramic image of each wind chamber according to the multispectral flame image of each wind chamber; a flame area recognition module, used to use an adaptive threshold segmentation algorithm to perform flame area recognition and garbage area recognition on the flame panoramic image of each wind chamber; a wind chamber state determination module, used to determine the combustion state of the flame in each wind chamber and the air flow state between each wind chamber based on the recognition result of the flame area; based on the recognition result of the garbage area, determine the residence state of the garbage in each wind chamber; a wind chamber air supply parameter adjustment module, used to adjust the air supply parameters of each wind chamber according to the combustion state of the flame in each wind chamber, the air flow state between each wind chamber and the residence state of the garbage in each wind chamber; a prediction optimization module, used to monitor the multispectral flame images of each wind chamber in the waste incinerator in real time after adjustment, and predict the future state of each wind chamber in the waste incinerator through a deep learning network, so as to optimize the air volume distribution between each wind chamber and the combustion coordinated control parameters.

[0008] Based on the embodiments provided in the present application, by capturing the multispectral flame images of each wind chamber in a waste incinerator and generating a flame panoramic image, the combustion state and garbage retention state of each wind chamber can be fully and accurately reflected; the flame area and garbage area of ​​the flame panoramic image are identified by using an adaptive threshold segmentation algorithm, which can effectively overcome the subjectivity and error problems of traditional manual identification methods and improve the accuracy and reliability of identification; based on the identification results, the combustion state, wind blowby state and garbage retention state of the flame in each wind chamber can be accurately determined, thereby providing a scientific basis for adjusting the air supply parameters of each wind chamber and realizing precise control of the waste incinerator; in addition, by real-time monitoring of the adjusted multispectral flame image and using a deep learning network to predict the future state of each wind chamber, the air volume distribution and combustion coordinated control parameters between the wind chambers can be further optimized, the combustion efficiency and stability of the waste incinerator can be improved, and pollutant emissions can be reduced, which has significant economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0010] Figure 1 This is a flow chart of an optional method for optimizing the combustion of a waste incinerator based on machine vision according to an embodiment of the present application;

[0011] Figure 2This is a structural diagram of an optional machine vision-based combustion optimization control system for a waste incinerator according to an embodiment of the present application.

[0012] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Alternatively, if Figure 1 As shown, the present application provides a combustion optimization control method of a waste incinerator based on machine vision, comprising:

[0015] S101, capturing multispectral flame images of each wind chamber in the waste incinerator;

[0016] S102, generating a flame panoramic image of each wind chamber according to the multispectral flame image of each wind chamber;

[0017] S103, using an adaptive threshold segmentation algorithm to perform flame area recognition and garbage area recognition on the flame panoramic image of each wind chamber;

[0018] S104, based on the identification result of the flame area, determining the burning state of the flame in each wind chamber and the air flow state between the wind chambers; based on the identification result of the garbage area, determining the retention state of the garbage in each wind chamber;

[0019] S105, adjusting the air supply parameters of each wind chamber according to the burning state of the flame in each wind chamber, the air flow state between each wind chamber, and the retention state of garbage in each wind chamber;

[0020] S106, real-time monitoring of the multi-spectral flame images of each wind chamber in the waste incinerator after adjustment, and prediction of the future state of each wind chamber in the waste incinerator through a deep learning network to optimize the air volume distribution and combustion coordination control parameters between the wind chambers.

[0021] Based on the embodiments provided in the present application, by capturing the multispectral flame images of each wind chamber in a waste incinerator and generating a flame panoramic image, the combustion state and garbage retention state of each wind chamber can be fully and accurately reflected; the flame area and garbage area of ​​the flame panoramic image are identified by using an adaptive threshold segmentation algorithm, which can effectively overcome the subjectivity and error problems of traditional manual identification methods and improve the accuracy and reliability of identification; based on the identification results, the combustion state, wind blowby state and garbage retention state of the flame in each wind chamber can be accurately determined, thereby providing a scientific basis for adjusting the air supply parameters of each wind chamber and realizing precise control of the waste incinerator; in addition, by real-time monitoring of the adjusted multispectral flame image and using a deep learning network to predict the future state of each wind chamber, the air volume distribution and combustion coordinated control parameters between the wind chambers can be further optimized, the combustion efficiency and stability of the waste incinerator can be improved, and pollutant emissions can be reduced, which has significant economic and environmental benefits.

[0022] Alternatively, if Figure 2 As shown, the present application provides a combustion optimization control system of a waste incinerator based on machine vision, comprising:

[0023] The flame image capturing module 201 is used to capture the multi-spectral flame image of each wind chamber in the waste incinerator;

[0024] The flame panoramic image generating module 202 is used to generate the flame panoramic image of each wind chamber according to the multi-spectral flame image of each wind chamber;

[0025] The flame region identification module 203 is used to use an adaptive threshold segmentation algorithm to perform flame region identification and garbage region identification on the flame panoramic image of each wind chamber;

[0026] The air chamber state determination module 204 is used to determine the burning state of the flame in each air chamber and the air flow state between each air chamber based on the identification result of the flame area; and determine the retention state of the garbage in each air chamber based on the identification result of the garbage area;

[0027] The air supply parameter adjustment module 205 is used to adjust the air supply parameters of each air chamber according to the burning state of the flame in each air chamber, the air flow state between each air chamber and the retention state of garbage in each air chamber;

[0028] The prediction and optimization module 206 is used to monitor the multi-spectral flame images of each wind chamber in the waste incinerator in real time after adjustment, and predict the future state of each wind chamber in the waste incinerator through a deep learning network to optimize the air volume distribution and combustion coordination control parameters between the wind chambers.

[0029] Among them, the air supply parameters of each wind chamber mainly focus on the air supply characteristics inside a single wind chamber. The air supply parameters of each wind chamber refer to the specific air supply indicators set for each wind chamber, such as air volume, wind pressure, wind speed, etc. These parameters determine the air supply intensity and air supply effect of a single wind chamber, and directly affect the combustion of garbage in the wind chamber. For example, when the garbage in a certain wind chamber is burning more vigorously, it may be necessary to increase the air supply volume of the wind chamber to provide sufficient oxygen to support combustion; or adjust the wind pressure so that the wind force can penetrate the garbage layer more deeply and promote uniform combustion.

[0030] The air volume distribution and combustion coordinated control parameters between each wind chamber mainly focus on the coordinated relationship and overall optimization between multiple wind chambers. The air volume distribution and combustion coordinated control parameters between each wind chamber include the coordination of air volume distribution ratio and combustion control strategy. The air volume distribution ratio refers to the relative size relationship between the air volumes of different wind chambers to achieve the balance of overall combustion and maximize efficiency; the combustion coordinated control parameters involve the coordinated control of multiple wind chambers during the combustion process, such as adjusting the air supply parameters and combustion strategies of each wind chamber synchronously according to the distribution and combustion state of the garbage to achieve the best overall combustion effect. For example, at a certain stage of the garbage incinerator, it may be necessary to allocate more air volume to the wind chambers where combustion is more difficult, while reducing the air volume of other wind chambers to concentrate resources to solve the combustion problem; or after the garbage enters the furnace, according to the movement and combustion of the garbage, the air supply parameters of each wind chamber are dynamically adjusted to make the combustion process smoothly transition between different wind chambers to avoid local overheating or inadequate combustion.

[0031] The air supply parameters of each air chamber, the air volume distribution between each air chamber, and the combustion coordination control parameters describe the system's optimization control strategy for the combustion process of the waste incinerator from a local and overall perspective. By simultaneously considering the air supply characteristics of a single air chamber and the coordination relationship between multiple air chambers, combustion optimization control can be more comprehensively achieved, the combustion efficiency and stability of the waste incinerator can be improved, pollutant emissions can be reduced, and the service life of the equipment can be extended.

[0032] Furthermore, the flame image capturing module includes at least two light sources of different wavelengths; wherein the at least two light sources of different wavelengths include a light source of a visible light band and a light source of a near infrared band;

[0033] The flame image capture module is used to capture images of each wind chamber in the garbage incinerator under different spectrums using at least two light sources with different wavelengths, so as to obtain a multi-spectral flame image of each wind chamber;

[0034] The flame panoramic image generation module is used to fuse the multi-spectral flame images of each wind chamber through an image fusion algorithm to obtain the flame panoramic image of each wind chamber; wherein the flame panoramic image of each wind chamber contains multiple spectral information.

[0035] Further, the flame area recognition result includes the flame area image corresponding to each wind chamber; the garbage area recognition result includes the garbage area image corresponding to each wind chamber; the flame area recognition module uses an adaptive threshold segmentation algorithm to perform flame area recognition and garbage area recognition on the flame panoramic image of each wind chamber, and is configured as follows:

[0036] In the flame panoramic image of each wind chamber containing multiple spectral information, the red component and saturation characteristics of the flame in each wind chamber and the reflectance spectrum characteristics of the garbage in each wind chamber are extracted;

[0037] Based on the color distribution characteristics of flames and the spectral reflectance characteristics of garbage, the red component and saturation characteristics of the flames in each wind chamber, as well as the reflectance spectrum characteristics of the garbage in each wind chamber are processed to establish color models of the flame area and garbage area in each wind chamber;

[0038] It should be understood that the wavelength of light emitted by light sources in the visible light band ranges from 400 nanometers to 700 nanometers, and the light in this range can be directly perceived by the human eye. The RGB color space represents colors based on the human eye's ability to perceive red, green, and blue light. Therefore, the RGB color space is mainly used to represent colors in the visible light band. The HSV (hue, saturation, brightness) and HSI (hue, saturation, intensity) color spaces are another form of representation of the RGB color space, which decompose colors into three independent components: hue, saturation, and brightness / intensity.

[0039] Based on the color distribution characteristics of the flame and the spectral reflectance characteristics of the garbage, the red component and saturation characteristics of the flame in each wind chamber are analyzed, including: in the RGB color space, the red component of the flame, that is, the pixel value of the red channel, is extracted. Flames usually have a higher red component because the color of the flame is mainly red and orange. By setting the threshold of the red component, the flame area can be preliminarily screened out; in the HSV or HSI color space, the saturation feature is extracted, that is, the purity or brightness of the color. The saturation of the flame is usually high, which means that the color of the flame is very bright. The saturation feature can help further distinguish flames from other objects with similar colors, such as red walls or red clothes;

[0040] Among them, the color model of the flame area and the garbage area is used to distinguish the flame area from the non-flame area and the garbage area from the non-garbage area in each wind chamber;

[0041] The gray-level co-occurrence matrix is ​​used to extract the texture features of the flame area and the garbage area in each wind chamber, such as contrast, energy, and uniformity. The texture features are helpful to distinguish the dynamic changes of the flame and the static distribution of the garbage, as well as to identify different types of garbage.

[0042] The Sobel operator is used to determine the shape boundary of the flame area and the shape boundary of the garbage area in each wind chamber based on the texture characteristics of the flame area and the texture characteristics of the garbage area in each wind chamber;

[0043] Among them, shape boundary features include not only contour features, but also the description of the entire shape area. It involves the geometric properties and spatial distribution characteristics of the shape, such as the direction, curvature, length, etc. of the boundary; Fourier shape descriptors and other methods can be used to describe the shape boundary;

[0044] The adaptive threshold corresponding to the flame panoramic image of each wind chamber is calculated using the maximum inter-class variance algorithm; wherein the maximum inter-class variance algorithm is used to automatically determine the optimal threshold according to the grayscale histogram of the image to separate the flame area and the garbage area from the background;

[0045] According to the color models of the flame area and the garbage area, the texture features of the flame area and the garbage area in each wind chamber, and the shape boundaries of the flame area and the garbage area in each wind chamber, the adaptive threshold corresponding to the flame panoramic image of each wind chamber is adjusted; for example, for the flame area with obvious color features, the threshold is appropriately lowered to retain more details; and for the garbage area with complex texture, the threshold is appropriately increased to reduce noise;

[0046] Based on the adjusted adaptive threshold corresponding to the flame panoramic image of each wind chamber, the flame panoramic image of each wind chamber is segmented to obtain the flame area image and the garbage area image corresponding to each wind chamber.

[0047] Furthermore, based on the following formula, the adaptive threshold corresponding to the flame panoramic image of each wind chamber is adjusted according to the color model of the flame area and the garbage area, the texture features of the flame area and the garbage area in each wind chamber, and the shape boundary of the flame area and the garbage area in each wind chamber:

[0048]

[0049] Among them, T initial is the adaptive threshold calculated using the maximum inter-class variance algorithm; T adjust is the adjusted adaptive threshold; Contrast flame and Contrast total They represent the texture features of the flame area and the texture features of the flame panoramic image respectively; C flame and C gabb They represent the characteristic values ​​of the color model of the flame area and the characteristic values ​​of the color model of the garbage area respectively; P flame and P gabb They represent the shape boundary features of the flame area and the shape boundary features of the garbage area respectively; Ptotal Represents the shape boundary features of the flame panoramic image.

[0050] Further, based on the adjusted adaptive threshold corresponding to the flame panoramic image of each wind chamber, the flame panoramic image of each wind chamber is segmented to obtain the flame area image and the garbage area image corresponding to each wind chamber, which are configured as follows:

[0051] Applying the adjusted adaptive threshold corresponding to the flame panoramic image of each wind chamber to the flame panoramic image of each wind chamber to perform image segmentation to obtain a binary image of the flame area and a binary image of the garbage area;

[0052] Morphological processing is performed on the dynamic characteristics of the image to optimize the boundary of the binary image of the flame area and the boundary of the binary image of the garbage area; wherein the morphological processing includes erosion operation and expansion operation to eliminate noise and fill small holes in the area;

[0053] The optimized binary image of the flame area is determined as the flame area image corresponding to each wind chamber, and the optimized binary image of the garbage area is determined as the garbage area image corresponding to each wind chamber.

[0054] Further, the burning state of the flame in each wind chamber includes the area change and flickering frequency of the flame in each wind chamber; the staying state of the garbage in each wind chamber includes the area fluctuation and accumulation; the wind chamber state determination module determines the burning state of the flame in each wind chamber and the air flow state between each wind chamber based on the recognition result of the flame area; determines the staying state of the garbage in each wind chamber based on the recognition result of the garbage area, and is configured as follows:

[0055] Acquire a plurality of continuous frames of flame area images corresponding to each wind chamber and a plurality of continuous frames of garbage area images corresponding to each wind chamber;

[0056] Frame difference analysis is performed on the continuous multiple-frame flame area images corresponding to each wind chamber to identify the difference areas between adjacent frames; the dynamic change area of ​​the flame in each wind chamber is determined through the identified difference area; wherein the dynamic change area is used to represent the movement and expansion of the flame in each wind chamber;

[0057] Based on the dynamic change area of ​​the flame in each wind chamber, the motion vector of the flame area is analyzed by using the optical flow method, and the area change and flickering frequency of the flame in each wind chamber are calculated; the area change includes the change speed and change direction;

[0058] If the flame area increases rapidly in a short period of time, it means that the combustion intensity has increased; if the area changes slightly or tends to be stable, it means that the combustion is relatively stable; analyze the flickering frequency of the flame area. The flickering frequency of the flame is closely related to the stability of the combustion. A high flickering frequency may indicate unstable combustion;

[0059] Analyze the area change and flickering frequency of the flame in each wind chamber to determine whether there is flame connectivity between adjacent wind chambers; if there is obvious movement or overlap of the flame between adjacent wind chambers, it indicates that there is wind cross-flow;

[0060] If it is determined whether there is flame connection between adjacent wind chambers, the area change and flickering frequency of the flame in each wind chamber are analyzed to identify the air flow disturbance characteristics between the wind chambers to determine the air flow state between the wind chambers; air flow disturbance may cause irregular changes in flame shape and displacement of position;

[0061] The garbage area images corresponding to each wind chamber are analyzed by frame difference method to track the shape and distribution characteristics of the garbage area;

[0062] The shape and distribution characteristics of the garbage area are used to determine the area fluctuation and accumulation of garbage in each wind chamber.

[0063] Monitor the fluctuation of the garbage area: if the garbage area remains stable in multiple consecutive frames, it means that the garbage is in a good state; if the area fluctuates greatly, it may mean that the garbage is unstable;

[0064] Garbage accumulation detection: Detects garbage accumulation by identifying the shape and distribution characteristics of the garbage area. Garbage accumulation can cause incomplete combustion and obstruction of airflow in the furnace.

[0065] Furthermore, the air supply parameters of each air chamber include air volume, air pressure, wind speed and air temperature; the air supply parameter adjustment module of each air chamber adjusts the air supply parameters of each air chamber according to the burning state of the flame in each air chamber, the air flow state between each air chamber and the retention state of garbage in each air chamber, and is configured as follows:

[0066] Adopting adaptive feature fusion algorithm, constructing a temporal convolution and bidirectional LSTM hybrid model; the temporal convolution and bidirectional LSTM hybrid model is the TCN-ABiLSTM hybrid deep neural network model;

[0067] in,

[0068] X = W × softmax[A × (p ref ; F flame ; F leak ; F stay )]

[0069] Among them, X is the input vector of the temporal convolution and bidirectional LSTM hybrid model. The input vector integrates the reference air supply parameters, the characteristic sequence of the combustion state of the flame in each air chamber, the characteristic sequence of the crossflow state between each air chamber, and the characteristic sequence of the garbage retention state in each air chamber. The reference air supply parameters are the air supply parameters of each air chamber before adjustment; W is a learnable weight matrix used to adjust the importance of different features in the model input; A is a feature association matrix used to capture the relationship between different features; softmax is a normalization function; p ref is the reference air supply parameter vector; F flame is the flame combustion state characteristic sequence matrix; F leak is the characteristic sequence matrix of the wind-through state, F stay is the garbage residence state feature sequence matrix; (p ref ; F flame ; F leak ; F stay ) is a characteristic vector formed by sequentially concatenating the reference air supply parameter vector and various state characteristic sequence matrices;

[0070] Among them, the output of the time convolution and bidirectional LSTM hybrid model is the time series prediction value of the garbage residence time, the time series prediction value of the turbulence degree, and the time series prediction value of the air volume ratio;

[0071] Among them, the residence time of garbage: ensure that the residence time of garbage in the furnace is greater than the total time required for theoretical drying, thermal decomposition and combustion, so as to ensure that the gaseous combustibles in the flue gas are completely burned;

[0072] Turbulence: An indicator that characterizes the degree of mixing between garbage and air. The greater the turbulence, the better the mixing and the more complete the combustion reaction.

[0073] Air volume ratio: the ratio of actual air volume to theoretical air volume, i.e. excess air coefficient, has a great influence on the burning condition of garbage;

[0074] The temporal convolution and bidirectional LSTM hybrid model includes a temporal convolution network layer and a bidirectional LSTM layer; the temporal convolution network layer is used to extract the sequence features of the data; the bidirectional LSTM layer is used to mine the temporal features of the data using the attention mechanism;

[0075] Initialize the gray wolf population, where each individual gray wolf in the gray wolf population represents a set of candidate solutions for air supply parameters;

[0076] The parameters of the improved gray wolf optimization algorithm are set, and the parameters of the improved gray wolf optimization algorithm include population size and number of iterations; a chaotic initialization strategy is introduced to make the gray wolf individuals evenly distributed in the search domain; wherein, the improved gray wolf optimization algorithm is Improved Grey Wolf Optimizer, IGWO;

[0077] The fitness value of each individual gray wolf is calculated according to the fitness function; wherein the fitness function is determined according to the following parameters: the target value of the residence time of the garbage, the target value of the turbulence, and the target value of the air volume ratio, and the time series prediction value of the residence time of the garbage, the time series prediction value of the turbulence, and the time series prediction value of the air volume ratio output by the time convolution and bidirectional LSTM hybrid model;

[0078] Sort the gray wolf individuals according to their fitness values, and select the three individuals with the highest fitness as the leading wolves;

[0079] Update the position of each individual gray wolf to move toward the position of the leader wolf to simulate hunting behavior;

[0080] Through the position update of each individual gray wolf, the optimal solution of air supply parameters is explored;

[0081] The optimal solution of the explored air supply parameters is determined as the adjusted air supply parameters of each air chamber.

[0082] Furthermore, in the process of updating the position of each individual gray wolf, a chaotic perturbation mechanism based on Logistic mapping is introduced;

[0083]

[0084] E ij =μ×E ij ×(1-E ij )

[0085] in, is the updated air supply parameter vector of the ith gray wolf individual, indicating the updated value of the air supply parameter of the ith gray wolf individual after iterative update, reflecting the new position of the ith gray wolf individual in the search domain; q i is the air supply parameter vector of the i-th gray wolf individual, indicating the air supply parameter combination of the i-th gray wolf individual in the current iteration; q leader is the air supply parameter vector of the leader wolf, representing the current optimal solution and serving as the target direction for other individuals to update their positions; B is the coefficient vector, used to control the step length of the individual moving toward the leader wolf position, which gradually decreases during the iteration process; D is the weight vector, used to adjust the degree of influence of the difference between the individual and the leader wolf; E i is the chaotic disturbance vector, which is generated by Logistic mapping and provides independent disturbance for each air supply parameter; μ is the control parameter of Logistic mapping; , which is usually taken between [3.57,4] to ensure that the system is in a chaotic state, thus generating a chaotic sequence with ergodicity and randomness; E ijis the jth element of the chaotic disturbance vector, indicating the disturbance intensity of the jth air supply parameter, and is generated iteratively through Logistic mapping. This formula enhances the global search capability of the Grey Wolf optimization algorithm by introducing chaotic disturbances, which helps to find a better combination of air supply parameters, thereby improving the combustion efficiency of the waste incinerator.

[0086] Furthermore, the future state of each wind chamber in the waste incinerator includes the future combustion state of the flame in each wind chamber, the future air flow state between each wind chamber, and the future retention state of the garbage in each wind chamber; the prediction optimization module monitors the multi-spectral flame image of each wind chamber in the waste incinerator in real time after adjustment, and predicts the future state of each wind chamber in the waste incinerator through a deep learning network to optimize the air volume distribution and combustion coordinated control parameters between each wind chamber, and is configured as follows:

[0087] Real-time monitoring of the multi-spectral flame images of each wind chamber in the waste incinerator after adjustment;

[0088] Based on the adjusted multispectral flame images of each wind chamber in the waste incinerator, the reference combustion state of the flame in each wind chamber, the reference blowby state between the wind chambers, and the reference residence state of the garbage in each wind chamber are determined;

[0089] Through the deep learning network, based on the reference combustion state of the flame in each wind chamber, the reference crossflow state between the wind chambers and the reference residence state of the garbage in each wind chamber, the future combustion state of the flame in each wind chamber, the future crossflow state between the wind chambers and the future residence state of the garbage in each wind chamber are predicted to optimize the air volume distribution between the wind chambers and the combustion coordinated control parameters.

[0090] It should be noted that in the present application, the embodiments implemented on the combustion optimization control system side of the waste incinerator based on machine vision can be cross-referenced with the embodiments implemented on the combustion optimization control method side of the waste incinerator based on machine vision, and this application will not go into details one by one.

[0091] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the combustion of a waste incinerator based on machine vision, characterized in that: include: Capturing multispectral flame images of each wind chamber in a waste incinerator; Generate a panoramic flame image of each wind chamber according to the multi-spectral flame image of each wind chamber; Use adaptive threshold segmentation algorithm to identify flame areas and garbage areas in the flame panoramic image of each wind chamber; Based on the identification results of the flame area, the burning state of the flame in each wind chamber and the air flow state between the wind chambers are determined; based on the identification results of the garbage area, the retention state of the garbage in each wind chamber is determined; Adjust the air supply parameters of each wind chamber according to the burning state of the flame in each wind chamber, the air flow state between each wind chamber, and the retention state of garbage in each wind chamber; The multispectral flame images of each wind chamber in the waste incinerator are monitored in real time after adjustment, and the future state of each wind chamber in the waste incinerator is predicted through a deep learning network to optimize the air volume distribution and combustion coordinated control parameters between the wind chambers.

2. A combustion optimization control system for a waste incinerator based on machine vision, the system implementing the method according to claim 1, characterized in that: include: A flame image capture module, used to capture multi-spectral flame images of each wind chamber in the waste incinerator; A flame panoramic image generation module is used to generate a flame panoramic image of each wind chamber according to the multi-spectral flame image of each wind chamber; The flame area recognition module is used to use an adaptive threshold segmentation algorithm to perform flame area recognition and garbage area recognition on the flame panoramic image of each wind chamber; The air chamber state determination module is used to determine the burning state of the flame in each air chamber and the air flow state between each air chamber based on the identification result of the flame area; and determine the retention state of the garbage in each air chamber based on the identification result of the garbage area; The air supply parameter adjustment module of the air chamber is used to adjust the air supply parameters of each air chamber according to the burning state of the flame in each air chamber, the air flow state between each air chamber and the retention state of garbage in each air chamber; The prediction and optimization module is used to monitor the multi-spectral flame images of each wind chamber in the waste incinerator in real time after adjustment, and predict the future state of each wind chamber in the waste incinerator through a deep learning network to optimize the air volume distribution and combustion coordinated control parameters between the wind chambers.

3. The combustion optimization control system of a waste incinerator based on machine vision according to claim 2 is characterized in that: The flame image capturing module includes at least two light sources with different wavelengths; wherein the at least two light sources with different wavelengths include a light source in the visible light band and a light source in the near infrared band; The flame image capture module is used to capture images of each wind chamber in the waste incinerator under different spectrums using at least two light sources with different wavelengths, so as to obtain a multi-spectral flame image of each wind chamber; The flame panoramic image generation module is used to fuse the multi-spectral flame images of each wind chamber through an image fusion algorithm to obtain the flame panoramic image of each wind chamber; wherein the flame panoramic image of each wind chamber contains multiple spectral information.

4. The combustion optimization control system of a waste incinerator based on machine vision according to claim 3 is characterized in that: The flame area recognition result includes the flame area image corresponding to each wind chamber; the garbage area recognition result includes the garbage area image corresponding to each wind chamber; the flame area recognition module uses an adaptive threshold segmentation algorithm to perform flame area recognition and garbage area recognition on the flame panoramic image of each wind chamber, and is configured as follows: In the flame panoramic image of each wind chamber containing multiple spectral information, the red component and saturation characteristics of the flame in each wind chamber and the reflectance spectrum characteristics of the garbage in each wind chamber are extracted; Based on the color distribution characteristics of flames and the spectral reflectance characteristics of garbage, the red component and saturation characteristics of the flames in each wind chamber, as well as the reflectance spectrum characteristics of the garbage in each wind chamber are processed to establish color models of the flame area and garbage area in each wind chamber; The color model of the flame area and the garbage area is used to distinguish the flame area from the non-flame area and the garbage area from the non-garbage area in each wind chamber; The texture features of the flame area and the garbage area in each wind chamber are extracted using the gray level co-occurrence matrix; The Sobel operator is used to determine the shape boundary of the flame area and the shape boundary of the garbage area in each wind chamber based on the texture characteristics of the flame area and the texture characteristics of the garbage area in each wind chamber; The adaptive threshold corresponding to the flame panoramic image of each wind chamber is calculated using the maximum inter-class variance algorithm; wherein the maximum inter-class variance algorithm is used to automatically determine the optimal threshold according to the grayscale histogram of the image to separate the flame area and the garbage area from the background; According to the color models of the flame area and the garbage area, the texture features of the flame area and the garbage area in each wind chamber, and the shape boundaries of the flame area and the garbage area in each wind chamber, the adaptive threshold corresponding to the flame panoramic image of each wind chamber is adjusted; Based on the adjusted adaptive threshold corresponding to the flame panoramic image of each wind chamber, the flame panoramic image of each wind chamber is segmented to obtain the flame area image and the garbage area image corresponding to each wind chamber.

5. The combustion optimization control system of a waste incinerator based on machine vision according to claim 4 is characterized in that: Based on the following formula, the adaptive threshold corresponding to the flame panoramic image of each wind chamber is adjusted according to the color model of the flame area and the garbage area, the texture characteristics of the flame area and the garbage area in each wind chamber, and the shape boundary of the flame area and the garbage area in each wind chamber.

6. The combustion optimization control system of a waste incinerator based on machine vision according to claim 4 is characterized in that: The flame panoramic images of each wind chamber are segmented based on the adaptive thresholds corresponding to the adjusted flame panoramic images of each wind chamber to obtain flame area images and garbage area images corresponding to each wind chamber, and are configured as follows: Applying the adjusted adaptive threshold corresponding to the flame panoramic image of each wind chamber to the flame panoramic image of each wind chamber to perform image segmentation to obtain a binary image of the flame area and a binary image of the garbage area; Performing morphological processing on the dynamic characteristics of the image to optimize the boundary of the binary image of the flame area and the boundary of the binary image of the garbage area; wherein the morphological processing includes an erosion operation and an expansion operation; The optimized binary image of the flame area is determined as the flame area image corresponding to each wind chamber, and the optimized binary image of the garbage area is determined as the garbage area image corresponding to each wind chamber.

7. The combustion optimization control system of a waste incinerator based on machine vision according to claim 6 is characterized in that: The burning state of the flame in each wind chamber includes the area change and flickering frequency of the flame in each wind chamber; the staying state of the garbage in each wind chamber includes the area fluctuation and accumulation; the wind chamber state determination module determines the burning state of the flame in each wind chamber and the air flow state between each wind chamber based on the recognition result of the flame area; determines the staying state of the garbage in each wind chamber based on the recognition result of the garbage area, and is configured as follows: Acquire a plurality of continuous frames of flame area images corresponding to each wind chamber and a plurality of continuous frames of garbage area images corresponding to each wind chamber; Perform frame difference analysis on the continuous multiple-frame flame area images corresponding to each wind chamber to identify the difference area between adjacent frames; determine the dynamic change area of ​​the flame in each wind chamber through the identified difference area; wherein the dynamic change area is used to represent the movement and expansion of the flame in each wind chamber; Based on the dynamic change area of ​​the flame in each wind chamber, the motion vector of the flame area is analyzed by using the optical flow method to calculate the area change and flickering frequency of the flame in each wind chamber; wherein the area change includes the change speed and change direction; Analyze the area change and flickering frequency of the flame in each wind chamber to determine whether there is flame connection between adjacent wind chambers; If it is determined whether there is flame connection between adjacent wind chambers, the area change and flickering frequency of the flame in each wind chamber are analyzed to identify the air flow disturbance characteristics between the wind chambers to determine the air flow status between the wind chambers; The garbage area images corresponding to each wind chamber are analyzed by frame difference method to track the shape and distribution characteristics of the garbage area; The shape and distribution characteristics of the garbage area are used to determine the area fluctuation and accumulation of garbage in each wind chamber.

8. The combustion optimization control system of a waste incinerator based on machine vision according to claim 7 is characterized in that: The air supply parameters of each air chamber include air volume, air pressure, wind speed and air temperature; the air supply parameter adjustment module of the air chamber adjusts the air supply parameters of each air chamber according to the burning state of the flame in each air chamber, the air flow state between each air chamber and the retention state of garbage in each air chamber, and is configured as follows: Adopting adaptive feature fusion algorithm, constructing a hybrid model of temporal convolution and bidirectional LSTM; The output of the time convolution and bidirectional LSTM hybrid model is the time series prediction value of the residence time of garbage, the time series prediction value of turbulence and the time series prediction value of air volume ratio; The temporal convolution and bidirectional LSTM hybrid model includes a temporal convolution network layer and a bidirectional LSTM layer; the temporal convolution network layer is used to extract the sequence characteristics of the data; the bidirectional LSTM layer is used to mine the temporal characteristics of the data using the attention mechanism; Initializing a gray wolf population, wherein each gray wolf individual in the gray wolf population represents a set of candidate solutions for air supply parameters; The parameters of the improved gray wolf optimization algorithm are set, including the population size and the number of iterations; a chaotic initialization strategy is introduced to make the gray wolf individuals evenly distributed in the search domain; The fitness value of each individual gray wolf is calculated according to the fitness function; wherein the fitness function is determined according to the following parameters: the target value of the residence time of the garbage, the target value of the turbulence, and the target value of the air volume ratio, and the time series prediction value of the residence time of the garbage, the time series prediction value of the turbulence, and the time series prediction value of the air volume ratio output by the time convolution and bidirectional LSTM hybrid model; Sort the gray wolf individuals according to their fitness values, and select the three individuals with the highest fitness as the leading wolves; Updating the position of each individual gray wolf so that it moves toward the position of the leading wolf to simulate hunting behavior; Through the position update of each individual gray wolf, the optimal solution of air supply parameters is explored; The optimal solution of the explored air supply parameters is determined as the adjusted air supply parameters of each air chamber.

9. The combustion optimization control system of a waste incinerator based on machine vision according to claim 8 is characterized in that: In the process of updating the position of each gray wolf, a chaotic perturbation mechanism based on Logistic mapping is introduced.

10. The combustion optimization control system of a waste incinerator based on machine vision according to claim 2 is characterized in that: The future state of each wind chamber in the waste incinerator includes the future combustion state of the flame in each wind chamber, the future air flow state between each wind chamber, and the future retention state of garbage in each wind chamber; the prediction optimization module monitors the adjusted multi-spectral flame image of each wind chamber in the waste incinerator in real time, predicts the future state of each wind chamber in the waste incinerator through a deep learning network, so as to optimize the air volume distribution and combustion coordinated control parameters between each wind chamber, and is configured as follows: Real-time monitoring of the adjusted multi-spectral flame images of each wind chamber in the waste incinerator; Based on the adjusted multispectral flame images of each wind chamber in the waste incinerator, determining a reference combustion state of the flame in each wind chamber, a reference blowby state between each wind chamber, and a reference residence state of the garbage in each wind chamber; Through the deep learning network, based on the reference combustion state of the flame in each wind chamber, the reference crossflow state between the wind chambers and the reference residence state of garbage in each wind chamber, the future combustion state of the flame in each wind chamber, the future crossflow state between the wind chambers and the future residence state of garbage in each wind chamber are predicted to optimize the air volume distribution and combustion coordinated control parameters between the wind chambers.

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